mjca                   package:ca                   R Documentation

_M_u_l_t_i_p_l_e _a_n_d _j_o_i_n_t _c_o_r_r_e_s_p_o_n_d_e_n_c_e _a_n_a_l_y_s_i_s

_D_e_s_c_r_i_p_t_i_o_n:

     Computation of multiple and joint correspondence analysis.

_U_s_a_g_e:

     mjca(obj, nd = 2, lambda = "adjusted", supcol = NA, subsetcol = NA, 
          ps = "", maxit = 50, epsilon = 0.0001)

_A_r_g_u_m_e_n_t_s:

obj      : A response pattern matrix (data frame containing factors).

nd       : Number of dimensions to be included in the output; if NA the
          maximum possible dimensions are included.

lambda   : Gives the scaling method. Possible values include
          '"indicator"', '"Burt"', '"adjusted"' and '"JCA"'. Using
          'lambda = "JCA"' results in a joint correspondence analysis
          using iterative adjusment of the Burt matrix in the solution
          space.

supcol   : Indices of supplementary columns.

subsetcol: Indices of subset categories.

ps       : Separator used for combining variable and category names.

maxit    : The maximum number of iterations (Joint Correspondence
          Analysis).

epsilon  : A convergence criterion (Joint Correspondence Analysis).

_D_e_t_a_i_l_s:

     The function 'mjca' computes a multiple or joint correspondence
     analysis based on the eigenvalue decomposition of the Burt matrix.

_V_a_l_u_e:

sv         : Eigenvalues ('lambda = "indicator"') or singular values
          ('lambda = "Burt"', '"adjusted"' or '"JCA"') 

lambda     : Scaling method

inertia.e  : Percentages of explained inertia

inertia.t  : Total inertia

inertia.et : Total percentage of explained inertia with the
          'nd'-dimensional solution

levelnames : Names of the factor/level combinations

levels.n   : Number of levels in each factor

nd         : User-specified dimensionality of the solution

nd.max     : Maximum possible dimensionality of the solution

rownames   : Row names

rowmass    : Row masses

rowdist    : Row chi-square distances to centroid

rowinertia : Row inertias

rowcoord   : Row standard coordinates

colnames   : Column names

colmass    : Column masses

coldist    : Column chi-square distances to centroid

colinertia : Column inertias

colcoord   : Column standard coordinates

colsup     : Indices of column supplementary points (of the Burt and
          Indicator matrix)

subsetcol  : Indices of subset columns

Burt       : Burt matrix

Burt.upd   : The updated Burt matrix (JCA only)

subinertia : Inertias of sub-matrices

JCA.iter   : Vector of length two containing the number of iterations
          and the epsilon (JCA only)

call       : Return of 'match.call'

_R_e_f_e_r_e_n_c_e_s:

     Nenadic, O. and Greenacre, M. (2007), Correspondence analysis in
     R, with two- and three-dimensional graphics: The ca package.
     Journal of Statistical Software, 20 (3), available at <URL:
     http://www.jstatsoft.org/v20/i03/>
      Nenadic, O. and Greenacre, M. (2007), Computation of Multiple
     Correspondence Analysis, with Code in R, in Multiple
     Correspondence Analysis and Related Methods (eds. M. Greenacre and
     J. Blasius), Chapmann & Hall / CRC, Boca Raton, London, New York,
     pp. 523-551.
      Greenacre, M.J. and Pardo, R. (2006), Subset correspondence
     analysis: visualizing relationships among a selected set of
     response categories from a questionnaire survey. Sociological
     Methods and Research, 35, pp. 193-218.

_S_e_e _A_l_s_o:

     'eigen', 'plot.mjca', 'summary.mjca', 'print.mjca'

_E_x_a_m_p_l_e_s:

      
     library(MASS)
     data(farms)
     mjca(farms)

     # Joint correspondence analysis:
     mjca(farms, lambda = "JCA")

      

